AI model deciphers the code in proteins that tells them where to go
Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.
Computer modeling
Validation technique could help scientists make more accurate forecasts
MIT researchers developed a new approach for assessing predictions with a spatial dimension, like forecasting weather or mapping air pollution.
For healthy hearing, timing matters
Machine-learning models let neuroscientists study the impact of auditory processing on real-world hearing.
Want to design the car of the future? Here are 8,000 designs to get you started.
MIT engineers developed the largest open-source dataset of car designs, including their aerodynamics, that could speed design of eco-friendly cars and electric vehicles.
Advancing urban tree monitoring with AI-powered digital twins
The Tree-D Fusion system integrates generative AI and genus-conditioned algorithms to create precise simulation-ready models of 600,000 existing urban trees across North America.
Modeling relationships to solve complex problems efficiently
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.
Machine learning unlocks secrets to advanced alloys
An MIT team uses computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more.
Making climate models relevant for local decision-makers
A new downscaling method leverages machine learning to speed up climate model simulations at finer resolutions, making them usable on local levels.
Scientists use generative AI to answer complex questions in physics
A new technique that can automatically classify phases of physical systems could help scientists investigate novel materials.
To build a better AI helper, start by modeling the irrational behavior of humans
A new technique can be used to predict the actions of human or AI agents who behave suboptimally while working toward unknown goals.